The construction of open-system diabatic Hamiltonians relevant for the investigation of electron transfer processes is a computational challenge. Here all relevant parameters for Redfield propagations are extracted from ab initio computations.

Dynamical simulations of ultrafast electron transfer reactions are of utmost interest. To allow for energy dissipation directly into an external surrounding environment, a solvent coupling model has been deduced, implemented, and utilized to describe the photoinduced electron transfer dynamics within a model triad system herein. The model is based on Redfield theory, and the environment is represented by harmonic oscillators filled with bosonic quanta. To imitate real solvents, the oscillators have been equipped with frequencies and polarization lifetimes characteristic of the corresponding solvent. The population was found to transfer through the energetically lowest electron transfer route regardless of the medium. The condensed population transfer dynamics were observed to be highly dependent on the solvent parameters. In particular, an increase in the solvent coupling entailed a detainment in the population transfer from the initially prepared diabatic state and a promotion in the population transfer through the other electron transfer route. Two explanations based on the diagonal and off-diagonal matrix elements of the Kohn−Sham Fock matrix, respectively, have been provided. The lifetime of the populated partially charge-separated state was prolonged with increasing solvent polarity, and it was explained in terms of attractive interactions between the solvent's dipole moments and the fragments' charges. The high-frequency vibrational fine-structure in the correlation function was demonstrated to be important for the transfer dynamics, and the importance of dephasing effects in polar solvents was verified and precised to concern the optical polarization of the solvents.

We explain why search algorithms can find molecules with particular properties in an enormous chemical space (ca 1060 molecules) by considering only a tiny subset (typically 103−6 molecules). Using a very simple example, we show that the number of potential paths that the search algorithms can follow to the target is equally vast. Thus, the probability of randomly finding a molecule that is on one of these paths is quite high and from here a search algorithm can follow the path to the target molecule. A path is defined as a series of molecules that have some non-zero quantifiable similarity (score) with the target molecule and that are increasingly similar to the target molecule. The minimum path length from any point in chemical space to the target corresponds is on the order of 100 steps, where a step is the change of and atom- or bond-type. Thus, a perfect search algorithm should be able to locate a particular molecule in chemical space by screening on the order of 100s of molecules, provided the score changes incrementally. We show that the actual number for a genetic search algorithm is between 100 and several millions, and depending on the target property and its dependence on molecular changes, the molecular representation, and the number of solutions to the search problem.

We attempt to explain why search algorithms can find molecules with particular properties in an enormous chemical space (ca 10 60 molecules) by considering only a tiny subset (typically 10 3−6 molecules). Using a very simple example, we show that the number of potential paths that the search algorithms can follow to the target is equally vast. Thus, the probability of randomly finding a molecule that is on one of these paths is quite high and from here a search algorithm can follow the path to the target molecule. A path is defined as a series of molecules that have some non-zero quantifiable similarity (score) with the target molecule and that are increasingly similar to the target molecule. The minimum path length from any point in chemical space to the target corresponds is on the order of 100 steps, where a step is the change of and atom-or bond-type. Thus, a perfect search algorithm should be able to locate a particular molecule in chemical space by screening on the order of 100s of molecules, provided the score changes incrementally. We show that the actual number for a genetic search algorithm is between 100 and several millions, and depending on the target property and its dependence on molecular changes, the molecular representation, and the number of solutions to the search problem.

We present a method for the automatic determination of transition states (TSs) that is based on Grimme’s RMSD-PP semiempirical tight binding reaction path method (J. Chem. Theory Comput. 2019, 15, 2847–2862), where the maximum energy structure along the path serves as an initial guess for DFT TS searches. The method is tested on 100 elementary reactions and located a total of 89 TSs correctly. Of the 11 remaining reactions, nine are shown not to be elementary reactions after all and for one of the two true failures the problem is shown to be the semiempirical tight binding model itself. Furthermore, we show that the GFN2-xTB RMSD-PP barrier is a good approximation for the corresponding DFT barrier for reactions with DFT barrier heights up to about 30 kcal/mol. Thus, GFN2-xTB RMSD-PP barrier heights, which can be estimated at the cost of a single energy minimisation, can be used to quickly identify reactions with low barriers, although it will also produce some false positives.